Data quality & preparation // key success factor in AI/NLP/RAG
Boring , but true— “garbage in, garbage out”, “gold in, gold out”:)
Here’s a summary of the webinar on best practices for data preparation in Retrieval-Augmented Generation (RAG):
Key Points:
1. The webinar focused on improving RAG systems through parsing and metadata extraction.
2. Two main components were discussed:
— Llama Parse: A document parser for complex documents (PDFs, PowerPoints, etc.)
— DY: A platform for generating high-quality, standardized hierarchical metadata
3. The importance of metadata in RAG:
— Helps filter large document sets quickly
— Improves retrieval accuracy, especially as data volume scales
— Allows for more efficient routing of queries
4. Experimental setup:
— Used 100 scientific research papers and 100 questions
— Compared three scenarios: basic parsing with PyPDF, Llama Parse without metadata, and Llama Parse with DY metadata
5. Key findings:
— Llama Parse improved accuracy over basic PyPDF parsing
— Adding metadata further improved retrieval accuracy, especially at larger data volumes
— Custom, domain-specific metadata had the most significant impact on accuracy
6. DY platform features:
— Can auto-suggest relevant metadata tags based on document content
— Allows for both chunk-level and document-level metadata
— Supports hierarchical metadata structures
7. Best practices:
— Use a combination of auto-suggested and manually defined metadata tags
— Consider using hierarchical metadata structures for diverse document sets
— Balance between generic and domain-specific metadata tags
8. Future considerations:
— Exploring ways to make metadata querying more efficient with vector databases
— Potential for dynamic metadata generation based on user interactions
The webinar emphasized the critical role of high-quality parsing and metadata in improving RAG system performance, especially as document volumes scale.
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a “retrieval as generation” strategy. Resources are available at https://github.com/FudanDNN-NLP/RAG.
